Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Binary regression for risks in excess of subject-specific thresholds.

H Zhang1, D Zelterman

  • 1Department of Epidemiology and Public Health, Yale University, New Haven, Connecticut 06520-8034, USA. heping.zhang@yale.edu

Biometrics
|April 21, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

EGFR mutation subtypes and response to immune checkpoint blockade treatment in non-small-cell lung cancer.

Annals of oncology : official journal of the European Society for Medical Oncology·2019
Same author

A dormant TIL phenotype defines non-small cell lung carcinomas sensitive to immune checkpoint blockers.

Nature communications·2018
Same author

A KRAS variant is a biomarker of poor outcome, platinum chemotherapy resistance and a potential target for therapy in ovarian cancer.

Oncogene·2011
Same author

WAG-F8(m1Ycb) rats harboring a factor VIII gene mutation provide a new animal model for hemophilia A.

Journal of thrombosis and haemostasis : JTH·2010
Same author

Pruritic and nociceptive sensations and dysesthesias from a spicule of cowhage.

Journal of neurophysiology·2009
Same author

The incidence and risk factors associated with postoperative delirium in geriatric patients undergoing surgery for suspected gynecologic malignancies.

Gynecologic oncology·2008
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces models to understand disease incidence based on risk factors, revealing individual risk tolerance influences disease probability. Findings from mouse models exposed to carcinogens provide insights into these biological mechanisms.

Area of Science:

  • Toxicology
  • Biostatistics
  • Epidemiology

Background:

  • Disease incidence is often linked to exposure to risk factors.
  • Individual responses to risk factors can vary due to unobservable tolerances.
  • Understanding these variations is crucial for accurate disease risk assessment.

Purpose of the Study:

  • To develop statistical models for binary data analyzing disease incidence.
  • To investigate the relationship between risk factor levels and disease occurrence.
  • To estimate parameters of individual risk tolerance and risk functions.

Main Methods:

  • Modeling binary-valued data to explain disease incidence.
  • Estimating parameters from tolerance distributions.
  • Analyzing data from a cohort of mice exposed to low-level carcinogens.

Related Experiment Videos

Main Results:

  • Developed models to explain disease incidence based on risk factor levels.
  • Estimated parameters for tolerance distribution and risk function.
  • Quantified the impact of low-level carcinogen exposure on disease incidence in mice.

Conclusions:

  • Individual risk tolerance is a key factor in disease incidence.
  • The developed models provide a framework for understanding risk-benefit relationships.
  • Low-level exposure to carcinogens can be modeled to predict disease risk.